Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations52413
Missing cells82132
Missing cells (%)9.8%
Duplicate rows895
Duplicate rows (%)1.7%
Total size in memory8.8 MiB
Average record size in memory176.3 B

Variable types

Numeric16

Alerts

Dataset has 895 (1.7%) duplicate rowsDuplicates
13171_FERM0101.Agitation_PV is highly overall correlated with 13171_FERM0101.Biocontainer_Pressure_PV and 4 other fieldsHigh correlation
13171_FERM0101.Biocontainer_Pressure_PV is highly overall correlated with 13171_FERM0101.Agitation_PV and 2 other fieldsHigh correlation
13171_FERM0101.DO_1_PV is highly overall correlated with 13171_FERM0101.Agitation_PVHigh correlation
13171_FERM0101.Gas_Overlay_PV is highly overall correlated with 13171_FERM0101.Agitation_PV and 2 other fieldsHigh correlation
13171_FERM0101.Load_Cell_Net_PV is highly overall correlated with 13171_FERM0101.Agitation_PV and 3 other fieldsHigh correlation
13171_FERM0101.pH_1_PV is highly overall correlated with 13171_FERM0101.Agitation_PV and 1 other fieldsHigh correlation
13171_FERM0101.Agitation_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.Air_Sparge_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.Biocontainer_Pressure_PV has 4656 (8.9%) missing valuesMissing
13171_FERM0101.DO_1_PV has 5628 (10.7%) missing valuesMissing
13171_FERM0101.DO_2_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.Gas_Overlay_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.Load_Cell_Net_PV has 4656 (8.9%) missing valuesMissing
13171_FERM0101.pH_1_PV has 9137 (17.4%) missing valuesMissing
13171_FERM0101.pH_2_PV has 6819 (13.0%) missing valuesMissing
13171_FERM0101.PUMP_1_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.PUMP_1_TOTAL has 4657 (8.9%) missing valuesMissing
13171_FERM0101.PUMP_2_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.PUMP_2_TOTAL has 4657 (8.9%) missing valuesMissing
13171_FERM0101.Single_Use_DO_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.Single_Use_pH_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.Temperatura_PV has 4658 (8.9%) missing valuesMissing
13171_FERM0101.DO_2_PV is highly skewed (γ1 = 41.28672561)Skewed
13171_FERM0101.PUMP_1_PV is highly skewed (γ1 = 114.6467451)Skewed
13171_FERM0101.PUMP_2_PV is highly skewed (γ1 = 53.8799871)Skewed
13171_FERM0101.Agitation_PV has 28492 (54.4%) zerosZeros
13171_FERM0101.Air_Sparge_PV has 46434 (88.6%) zerosZeros
13171_FERM0101.Biocontainer_Pressure_PV has 2479 (4.7%) zerosZeros
13171_FERM0101.DO_1_PV has 42142 (80.4%) zerosZeros
13171_FERM0101.DO_2_PV has 47727 (91.1%) zerosZeros
13171_FERM0101.Gas_Overlay_PV has 25342 (48.4%) zerosZeros
13171_FERM0101.Load_Cell_Net_PV has 640 (1.2%) zerosZeros
13171_FERM0101.PUMP_1_PV has 47749 (91.1%) zerosZeros
13171_FERM0101.PUMP_1_TOTAL has 7541 (14.4%) zerosZeros
13171_FERM0101.PUMP_2_PV has 47155 (90.0%) zerosZeros
13171_FERM0101.PUMP_2_TOTAL has 24586 (46.9%) zerosZeros

Reproduction

Analysis started2024-09-29 18:18:00.369095
Analysis finished2024-09-29 18:18:21.105214
Duration20.74 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

13171_FERM0101.Agitation_PV
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct453
Distinct (%)0.9%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean17.658302
Minimum0
Maximum80
Zeros28492
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:21.150842image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile72
Maximum80
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation25.926322
Coefficient of variation (CV)1.4682228
Kurtosis0.053574913
Mean17.658302
Median Absolute Deviation (MAD)0
Skewness1.2377167
Sum843272.21
Variance672.17418
MonotonicityNot monotonic
2024-09-29T20:18:21.230132image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28492
54.4%
20 7705
 
14.7%
72 5780
 
11.0%
44 2828
 
5.4%
80 576
 
1.1%
56 449
 
0.9%
32 250
 
0.5%
64 238
 
0.5%
24 192
 
0.4%
41.87200012 118
 
0.2%
Other values (443) 1127
 
2.2%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
0 28492
54.4%
9.19998938 1
 
< 0.1%
20 7705
 
14.7%
20.0199104 1
 
< 0.1%
20.09518522 1
 
< 0.1%
20.14830595 1
 
< 0.1%
20.2768615 1
 
< 0.1%
20.35507094 1
 
< 0.1%
20.40556412 1
 
< 0.1%
20.42562686 1
 
< 0.1%
ValueCountFrequency (%)
80 576
 
1.1%
72 5780
11.0%
71.99999059 1
 
< 0.1%
71.99992676 1
 
< 0.1%
71.99985962 1
 
< 0.1%
71.99979535 1
 
< 0.1%
71.99973145 1
 
< 0.1%
71.99966431 1
 
< 0.1%
71.99959998 1
 
< 0.1%
71.99953613 1
 
< 0.1%

13171_FERM0101.Air_Sparge_PV
Real number (ℝ)

MISSING  ZEROS 

Distinct1322
Distinct (%)2.8%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean0.20326867
Minimum0
Maximum16.002353
Zeros46434
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:21.304411image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16.002353
Range16.002353
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5983092
Coefficient of variation (CV)7.8630378
Kurtosis77.273577
Mean0.20326867
Median Absolute Deviation (MAD)0
Skewness8.7053742
Sum9707.0952
Variance2.5545923
MonotonicityNot monotonic
2024-09-29T20:18:21.376710image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46434
88.6%
1.427198992 1
 
< 0.1%
1.839859052 1
 
< 0.1%
2.283028517 1
 
< 0.1%
2.706106268 1
 
< 0.1%
2.537766799 1
 
< 0.1%
2.092278158 1
 
< 0.1%
0.1420342704 1
 
< 0.1%
1.265706164 1
 
< 0.1%
0.3037414331 1
 
< 0.1%
Other values (1312) 1312
 
2.5%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
0 46434
88.6%
0.0006503327287 1
 
< 0.1%
0.001388928302 1
 
< 0.1%
0.001551954602 1
 
< 0.1%
0.001727686448 1
 
< 0.1%
0.001995624152 1
 
< 0.1%
0.002754363406 1
 
< 0.1%
0.002982296654 1
 
< 0.1%
0.003166518988 1
 
< 0.1%
0.003293498541 1
 
< 0.1%
ValueCountFrequency (%)
16.00235327 1
< 0.1%
16.00225139 1
< 0.1%
16.00203551 1
< 0.1%
16.00188188 1
< 0.1%
16.00182802 1
< 0.1%
16.00176277 1
< 0.1%
16.00170209 1
< 0.1%
16.00156782 1
< 0.1%
16.00137721 1
< 0.1%
16.00135861 1
< 0.1%

13171_FERM0101.Biocontainer_Pressure_PV
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23591
Distinct (%)49.4%
Missing4656
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean207.2114
Minimum-17.466855
Maximum480
Zeros2479
Zeros (%)4.7%
Negative14825
Negative (%)28.3%
Memory size2.8 MiB
2024-09-29T20:18:21.450559image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-17.466855
5-th percentile-2.0755825
Q1-0.44592103
median1.8692139
Q3480
95-th percentile480
Maximum480
Range497.46685
Interquartile range (IQR)480.44592

Descriptive statistics

Standard deviation237.8902
Coefficient of variation (CV)1.1480556
Kurtosis-1.9244616
Mean207.2114
Median Absolute Deviation (MAD)3.8106837
Skewness0.27449576
Sum9895794.8
Variance56591.747
MonotonicityNot monotonic
2024-09-29T20:18:21.522394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
480 20627
39.4%
0 2479
 
4.7%
-1.736108398 36
 
0.1%
-0.3993041992 35
 
0.1%
-1.756365967 34
 
0.1%
-0.6221069336 29
 
0.1%
-0.4398132324 28
 
0.1%
-2.019677734 27
 
0.1%
-1.533563232 27
 
0.1%
-2.039929199 26
 
< 0.1%
Other values (23581) 24409
46.6%
(Missing) 4656
 
8.9%
ValueCountFrequency (%)
-17.46685468 1
< 0.1%
-17.40801237 1
< 0.1%
-17.40423101 1
< 0.1%
-17.40284387 1
< 0.1%
-17.39293823 1
< 0.1%
-17.38617579 1
< 0.1%
-17.36105496 1
< 0.1%
-17.36061863 1
< 0.1%
-17.33317647 1
< 0.1%
-17.21719521 1
< 0.1%
ValueCountFrequency (%)
480 20627
39.4%
477.1533083 1
 
< 0.1%
413.1858147 1
 
< 0.1%
308.5423424 1
 
< 0.1%
57.84815568 1
 
< 0.1%
8.722714819 1
 
< 0.1%
8.693263114 1
 
< 0.1%
8.574339883 1
 
< 0.1%
8.499377033 1
 
< 0.1%
8.383500725 1
 
< 0.1%

13171_FERM0101.DO_1_PV
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct4318
Distinct (%)9.2%
Missing5628
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean3.1067104
Minimum0
Maximum94.793915
Zeros42142
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:21.592777image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile19.211083
Maximum94.793915
Range94.793915
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.683093
Coefficient of variation (CV)3.7605993
Kurtosis23.372474
Mean3.1067104
Median Absolute Deviation (MAD)0
Skewness4.7046667
Sum145347.45
Variance136.49466
MonotonicityNot monotonic
2024-09-29T20:18:21.661177image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42142
80.4%
81.70202026 17
 
< 0.1%
72.82851563 14
 
< 0.1%
72.41400757 11
 
< 0.1%
72.14432983 11
 
< 0.1%
72.00949097 7
 
< 0.1%
15.60005035 7
 
< 0.1%
72.69367676 6
 
< 0.1%
71.99950562 6
 
< 0.1%
72.13933716 6
 
< 0.1%
Other values (4308) 4558
 
8.7%
(Missing) 5628
 
10.7%
ValueCountFrequency (%)
0 42142
80.4%
1.314477684 1
 
< 0.1%
1.394072316 1
 
< 0.1%
2.03551119 1
 
< 0.1%
2.074257469 1
 
< 0.1%
2.342163849 3
 
< 0.1%
2.406096907 1
 
< 0.1%
2.480812073 1
 
< 0.1%
2.619460297 1
 
< 0.1%
2.791164589 1
 
< 0.1%
ValueCountFrequency (%)
94.79391538 1
< 0.1%
91.0729121 1
< 0.1%
91.06054801 1
< 0.1%
89.45164153 1
< 0.1%
87.61194135 1
< 0.1%
87.39271376 1
< 0.1%
87.28655346 1
< 0.1%
85.68274537 1
< 0.1%
85.2845485 1
< 0.1%
85.19063381 1
< 0.1%

13171_FERM0101.DO_2_PV
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean0.052312737
Minimum0
Maximum89.740161
Zeros47727
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:21.719458image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum89.740161
Range89.740161
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1602491
Coefficient of variation (CV)41.294897
Kurtosis1703.2286
Mean0.052312737
Median Absolute Deviation (MAD)0
Skewness41.286726
Sum2498.1948
Variance4.6666761
MonotonicityNot monotonic
2024-09-29T20:18:21.772982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 47727
91.1%
89.58114014 9
 
< 0.1%
89.42779541 6
 
< 0.1%
89.74016113 4
 
< 0.1%
89.58363173 1
 
< 0.1%
89.63708413 1
 
< 0.1%
89.65920385 1
 
< 0.1%
89.73720789 1
 
< 0.1%
89.61322148 1
 
< 0.1%
89.59118919 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
0 47727
91.1%
79.86813354 1
 
< 0.1%
89.26877441 1
 
< 0.1%
89.42779541 6
 
< 0.1%
89.47863892 1
 
< 0.1%
89.58114014 9
 
< 0.1%
89.58363173 1
 
< 0.1%
89.59118919 1
 
< 0.1%
89.61322148 1
 
< 0.1%
89.63708413 1
 
< 0.1%
ValueCountFrequency (%)
89.74016113 4
< 0.1%
89.73720789 1
 
< 0.1%
89.65920385 1
 
< 0.1%
89.63708413 1
 
< 0.1%
89.61322148 1
 
< 0.1%
89.59118919 1
 
< 0.1%
89.58363173 1
 
< 0.1%
89.58114014 9
< 0.1%
89.47863892 1
 
< 0.1%
89.42779541 6
< 0.1%

13171_FERM0101.Gas_Overlay_PV
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22414
Distinct (%)46.9%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean1.976907
Minimum0
Maximum16.000695
Zeros25342
Zeros (%)48.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:21.833741image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.0000039
95-th percentile4.0004047
Maximum16.000695
Range16.000695
Interquartile range (IQR)4.0000039

Descriptive statistics

Standard deviation2.1973059
Coefficient of variation (CV)1.1114867
Kurtosis-0.61652318
Mean1.976907
Median Absolute Deviation (MAD)0
Skewness0.52267609
Sum94407.196
Variance4.8281533
MonotonicityNot monotonic
2024-09-29T20:18:21.907440image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25342
48.4%
4.000093447 1
 
< 0.1%
3.999817579 1
 
< 0.1%
3.999761204 1
 
< 0.1%
4.000020328 1
 
< 0.1%
4.000010258 1
 
< 0.1%
3.999660146 1
 
< 0.1%
4.0002661 1
 
< 0.1%
4.000117034 1
 
< 0.1%
3.999839784 1
 
< 0.1%
Other values (22404) 22404
42.7%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
0 25342
48.4%
0.02326878684 1
 
< 0.1%
0.5026381067 1
 
< 0.1%
0.5133354164 1
 
< 0.1%
0.5729348989 1
 
< 0.1%
1.26004482 1
 
< 0.1%
1.811821992 1
 
< 0.1%
1.893493225 1
 
< 0.1%
1.910756824 1
 
< 0.1%
1.949962749 1
 
< 0.1%
ValueCountFrequency (%)
16.00069479 1
< 0.1%
15.9998249 1
< 0.1%
15.99980576 1
< 0.1%
15.99952382 1
< 0.1%
15.99924665 1
< 0.1%
15.99864865 1
< 0.1%
14.97904898 1
< 0.1%
14.52046136 1
< 0.1%
12.81875877 1
< 0.1%
12.01324713 1
< 0.1%

13171_FERM0101.Load_Cell_Net_PV
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6360
Distinct (%)13.3%
Missing4656
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean54.85092
Minimum-8.6041119
Maximum191.92
Zeros640
Zeros (%)1.2%
Negative21259
Negative (%)40.6%
Memory size2.8 MiB
2024-09-29T20:18:21.983241image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-8.6041119
5-th percentile-7.1199997
Q1-6.4800003
median0.24000001
Q3158.4
95-th percentile166.88
Maximum191.92
Range200.52411
Interquartile range (IQR)164.88

Descriptive statistics

Standard deviation73.671666
Coefficient of variation (CV)1.3431254
Kurtosis-1.4802376
Mean54.85092
Median Absolute Deviation (MAD)7.2800002
Skewness0.58054064
Sum2619515.4
Variance5427.5144
MonotonicityNot monotonic
2024-09-29T20:18:22.059680image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.640000153 1326
 
2.5%
-6.559999847 1213
 
2.3%
-6.4 1107
 
2.1%
-7.040000153 1080
 
2.1%
-6.480000305 1071
 
2.0%
-6.8 1062
 
2.0%
-6.959999847 934
 
1.8%
-6.880000305 803
 
1.5%
-7.119999695 765
 
1.5%
-5.6 720
 
1.4%
Other values (6350) 37676
71.9%
(Missing) 4656
 
8.9%
ValueCountFrequency (%)
-8.604111888 1
 
< 0.1%
-8.4 1
 
< 0.1%
-8.319999695 1
 
< 0.1%
-8.272293295 1
 
< 0.1%
-8.159999847 3
 
< 0.1%
-8.137889231 1
 
< 0.1%
-8.081737327 1
 
< 0.1%
-8.080000305 3
 
< 0.1%
-8 7
< 0.1%
-7.919999695 10
< 0.1%
ValueCountFrequency (%)
191.9199951 1
< 0.1%
191.8400024 1
< 0.1%
191.7241006 1
< 0.1%
191.5688244 1
< 0.1%
191.4400024 1
< 0.1%
191.3599976 1
< 0.1%
175.8030514 1
< 0.1%
175.2523186 1
< 0.1%
174.830138 1
< 0.1%
174.5363098 1
< 0.1%

13171_FERM0101.pH_1_PV
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6148
Distinct (%)14.2%
Missing9137
Missing (%)17.4%
Infinite0
Infinite (%)0.0%
Mean3.0921634
Minimum-0.3285778
Maximum96.477242
Zeros66
Zeros (%)0.1%
Negative391
Negative (%)0.7%
Memory size2.8 MiB
2024-09-29T20:18:22.136924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.3285778
5-th percentile1.260704
Q11.4570414
median1.5985078
Q35.876836
95-th percentile6.1505007
Maximum96.477242
Range96.80582
Interquartile range (IQR)4.4197947

Descriptive statistics

Standard deviation2.1533841
Coefficient of variation (CV)0.69640048
Kurtosis80.063864
Mean3.0921634
Median Absolute Deviation (MAD)0.28655367
Skewness2.3936815
Sum133816.46
Variance4.6370631
MonotonicityNot monotonic
2024-09-29T20:18:22.208717image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.598507786 1687
 
3.2%
1.38335743 1599
 
3.1%
1.569852829 1332
 
2.5%
1.975545883 1266
 
2.4%
1.457041359 853
 
1.6%
1.578411865 791
 
1.5%
1.57115612 768
 
1.5%
1.539599609 743
 
1.4%
1.102238846 735
 
1.4%
1.885061455 678
 
1.3%
Other values (6138) 32824
62.6%
(Missing) 9137
 
17.4%
ValueCountFrequency (%)
-0.3285778046 3
 
< 0.1%
-0.257396698 35
 
0.1%
-0.2403810501 343
0.7%
-0.1913319103 1
 
< 0.1%
-0.1461831317 1
 
< 0.1%
-0.1384361911 1
 
< 0.1%
-0.1354048353 1
 
< 0.1%
-0.1159889384 1
 
< 0.1%
-0.112992759 1
 
< 0.1%
-0.1046121073 1
 
< 0.1%
ValueCountFrequency (%)
96.47724174 1
< 0.1%
10.29741389 1
< 0.1%
8.851742664 1
< 0.1%
8.298103593 1
< 0.1%
7.606374086 1
< 0.1%
7.308830579 1
< 0.1%
7.18436231 1
< 0.1%
7.130503403 1
< 0.1%
6.828440791 1
< 0.1%
6.775617981 1
< 0.1%

13171_FERM0101.pH_2_PV
Real number (ℝ)

MISSING 

Distinct798
Distinct (%)1.8%
Missing6819
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean3.0720099
Minimum-0.016351318
Maximum6.0018169
Zeros23
Zeros (%)< 0.1%
Negative1402
Negative (%)2.7%
Memory size2.8 MiB
2024-09-29T20:18:22.277469image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-0.016351318
5-th percentile3.2
Q13.2
median3.2
Q33.2
95-th percentile3.2
Maximum6.0018169
Range6.0181683
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.60558011
Coefficient of variation (CV)0.1971283
Kurtosis20.356435
Mean3.0720099
Median Absolute Deviation (MAD)0
Skewness-4.6732482
Sum140065.22
Variance0.36672726
MonotonicityNot monotonic
2024-09-29T20:18:22.346965image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2 43380
82.8%
-0.01635131836 1396
 
2.7%
0 23
 
< 0.1%
2.157751059 1
 
< 0.1%
1.784685318 1
 
< 0.1%
2.029801508 1
 
< 0.1%
2.198046836 1
 
< 0.1%
1.604200996 1
 
< 0.1%
1.702661731 1
 
< 0.1%
1.467929778 1
 
< 0.1%
Other values (788) 788
 
1.5%
(Missing) 6819
 
13.0%
ValueCountFrequency (%)
-0.01635131836 1396
2.7%
-0.01155882534 1
 
< 0.1%
-0.01131186551 1
 
< 0.1%
-0.005097151927 1
 
< 0.1%
-0.005031424847 1
 
< 0.1%
-0.004830629337 1
 
< 0.1%
-0.00479302398 1
 
< 0.1%
0 23
 
< 0.1%
0.0001582356723 1
 
< 0.1%
0.0001582643674 1
 
< 0.1%
ValueCountFrequency (%)
6.00181694 1
 
< 0.1%
5.966472244 1
 
< 0.1%
3.2 43380
82.8%
3.19939067 1
 
< 0.1%
3.19925527 1
 
< 0.1%
3.198962573 1
 
< 0.1%
3.198836907 1
 
< 0.1%
3.1978056 1
 
< 0.1%
3.197653228 1
 
< 0.1%
3.197442338 1
 
< 0.1%

13171_FERM0101.PUMP_1_PV
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean0.0020647442
Minimum0
Maximum31.292846
Zeros47749
Zeros (%)91.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:22.404379image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum31.292846
Range31.292846
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21442789
Coefficient of variation (CV)103.85204
Kurtosis14101.39
Mean0.0020647442
Median Absolute Deviation (MAD)0
Skewness114.64675
Sum98.601859
Variance0.045979319
MonotonicityNot monotonic
2024-09-29T20:18:22.456787image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 47749
91.1%
9.696192385 1
 
< 0.1%
0.258633783 1
 
< 0.1%
31.29284561 1
 
< 0.1%
15.2311284 1
 
< 0.1%
22.36756257 1
 
< 0.1%
19.75549591 1
 
< 0.1%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
0 47749
91.1%
0.258633783 1
 
< 0.1%
9.696192385 1
 
< 0.1%
15.2311284 1
 
< 0.1%
19.75549591 1
 
< 0.1%
22.36756257 1
 
< 0.1%
31.29284561 1
 
< 0.1%
ValueCountFrequency (%)
31.29284561 1
 
< 0.1%
22.36756257 1
 
< 0.1%
19.75549591 1
 
< 0.1%
15.2311284 1
 
< 0.1%
9.696192385 1
 
< 0.1%
0.258633783 1
 
< 0.1%
0 47749
91.1%

13171_FERM0101.PUMP_1_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct880
Distinct (%)1.8%
Missing4657
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean36.590068
Minimum0
Maximum512.53447
Zeros7541
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:22.523162image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.4399994
median9.9199997
Q317.360001
95-th percentile99.199969
Maximum512.53447
Range512.53447
Interquartile range (IQR)9.9200012

Descriptive statistics

Standard deviation101.60514
Coefficient of variation (CV)2.7768502
Kurtosis16.982406
Mean36.590068
Median Absolute Deviation (MAD)4.9600006
Skewness4.2650105
Sum1747395.3
Variance10323.604
MonotonicityNot monotonic
2024-09-29T20:18:22.595798image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.43999939 8370
16.0%
0 7541
14.4%
17.36000061 6744
12.9%
14.88000031 5151
9.8%
9.919999695 4909
9.4%
12.4 4475
8.5%
4.959999847 2103
 
4.0%
96.7199707 2011
 
3.8%
512.5344727 1974
 
3.8%
6.61333313 529
 
1.0%
Other values (870) 3949
7.5%
(Missing) 4657
8.9%
ValueCountFrequency (%)
0 7541
14.4%
0.004837127198 1
 
< 0.1%
0.01336322829 1
 
< 0.1%
0.01717965255 1
 
< 0.1%
0.01790767497 1
 
< 0.1%
0.01814191661 1
 
< 0.1%
0.02674272099 1
 
< 0.1%
0.02921923892 1
 
< 0.1%
0.03409715752 1
 
< 0.1%
0.03927622952 1
 
< 0.1%
ValueCountFrequency (%)
512.5344727 1974
3.8%
399.7768887 1
 
< 0.1%
399.324047 1
 
< 0.1%
394.2282315 1
 
< 0.1%
361.9359012 1
 
< 0.1%
354.9669117 1
 
< 0.1%
353.294405 1
 
< 0.1%
351.8438728 1
 
< 0.1%
350.0818073 1
 
< 0.1%
350.0360569 1
 
< 0.1%

13171_FERM0101.PUMP_2_PV
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct594
Distinct (%)1.2%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean0.025990255
Minimum0
Maximum48
Zeros47155
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:22.667311image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum48
Range48
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40467109
Coefficient of variation (CV)15.570109
Kurtosis5200.303
Mean0.025990255
Median Absolute Deviation (MAD)0
Skewness53.879987
Sum1241.1646
Variance0.16375869
MonotonicityNot monotonic
2024-09-29T20:18:22.738650image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 47155
90.0%
8 3
 
< 0.1%
1.388485938 3
 
< 0.1%
0.001509094238 2
 
< 0.1%
1.38847309 2
 
< 0.1%
1.388485748 2
 
< 0.1%
4.287061064 1
 
< 0.1%
1.705317442 1
 
< 0.1%
4.799482481 1
 
< 0.1%
4.473761197 1
 
< 0.1%
Other values (584) 584
 
1.1%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
0 47155
90.0%
0.0001165716234 1
 
< 0.1%
0.0001920202796 1
 
< 0.1%
0.0004622735494 1
 
< 0.1%
0.000502589483 1
 
< 0.1%
0.0006660618071 1
 
< 0.1%
0.0008622826146 1
 
< 0.1%
0.0008802621543 1
 
< 0.1%
0.0009214454408 1
 
< 0.1%
0.0009459621342 1
 
< 0.1%
ValueCountFrequency (%)
48 1
 
< 0.1%
32.52447157 1
 
< 0.1%
20.06333269 1
 
< 0.1%
8 3
< 0.1%
7.890617317 1
 
< 0.1%
7.712123398 1
 
< 0.1%
7.534939796 1
 
< 0.1%
7.389618969 1
 
< 0.1%
7.204369783 1
 
< 0.1%
6.3912063 1
 
< 0.1%

13171_FERM0101.PUMP_2_TOTAL
Real number (ℝ)

MISSING  ZEROS 

Distinct1535
Distinct (%)3.2%
Missing4657
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean471.75443
Minimum0
Maximum11066.218
Zeros24586
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:22.808820image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3312.28723
95-th percentile2426.9166
Maximum11066.218
Range11066.218
Interquartile range (IQR)312.28723

Descriptive statistics

Standard deviation1142.7441
Coefficient of variation (CV)2.4223284
Kurtosis22.772506
Mean471.75443
Median Absolute Deviation (MAD)0
Skewness3.9644022
Sum22529104
Variance1305864.1
MonotonicityNot monotonic
2024-09-29T20:18:22.880265image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24586
46.9%
2426.916602 2439
 
4.7%
57.75947266 1703
 
3.2%
228.0223633 1377
 
2.6%
4360.263281 1257
 
2.4%
17.84088898 909
 
1.7%
128.7980713 837
 
1.6%
191.6427124 799
 
1.5%
3823.102734 772
 
1.5%
450.6058594 689
 
1.3%
Other values (1525) 12388
23.6%
(Missing) 4657
 
8.9%
ValueCountFrequency (%)
0 24586
46.9%
0.1173955137 1
 
< 0.1%
0.1750571234 1
 
< 0.1%
0.2849437213 1
 
< 0.1%
0.2863696957 1
 
< 0.1%
0.3985455648 1
 
< 0.1%
0.4192346391 1
 
< 0.1%
0.4267163687 1
 
< 0.1%
1.201073721 1
 
< 0.1%
1.297263096 1
 
< 0.1%
ValueCountFrequency (%)
11066.21797 130
 
0.2%
11044.89101 1
 
< 0.1%
9387.447535 1
 
< 0.1%
5444.042401 1
 
< 0.1%
4360.263281 1257
2.4%
4355.168542 1
 
< 0.1%
4353.605218 1
 
< 0.1%
4343.469875 1
 
< 0.1%
4328.185674 1
 
< 0.1%
4327.555406 1
 
< 0.1%

13171_FERM0101.Single_Use_DO_PV
Real number (ℝ)

MISSING 

Distinct6644
Distinct (%)13.9%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean674.70197
Minimum0
Maximum861.78174
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:22.950828image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.424482
Q1712.9418
median799.99199
Q3799.99199
95-th percentile834.80322
Maximum861.78174
Range861.78174
Interquartile range (IQR)87.050195

Descriptive statistics

Standard deviation261.32584
Coefficient of variation (CV)0.38732041
Kurtosis2.1977911
Mean674.70197
Median Absolute Deviation (MAD)17.995752
Skewness-1.9977722
Sum32220393
Variance68291.196
MonotonicityNot monotonic
2024-09-29T20:18:23.019438image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
799.9919922 21272
40.6%
712.9417969 3429
 
6.5%
834.8032227 1813
 
3.5%
723.647168 1620
 
3.1%
780.2516602 1343
 
2.6%
698.0123535 1313
 
2.5%
707.9575195 784
 
1.5%
786.0784668 779
 
1.5%
860.3914063 722
 
1.4%
797.5991699 692
 
1.3%
Other values (6634) 13988
26.7%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
0 4
< 0.1%
0.468882039 1
 
< 0.1%
0.5104694107 1
 
< 0.1%
0.6447309521 1
 
< 0.1%
0.6515499482 1
 
< 0.1%
0.6970039807 1
 
< 0.1%
0.7005806965 1
 
< 0.1%
0.7034765244 1
 
< 0.1%
0.7063981392 1
 
< 0.1%
0.7067219516 1
 
< 0.1%
ValueCountFrequency (%)
861.7817383 671
1.3%
861.6756228 1
 
< 0.1%
860.8538396 1
 
< 0.1%
860.8140773 1
 
< 0.1%
860.8031504 1
 
< 0.1%
860.7404356 1
 
< 0.1%
860.3914063 722
1.4%
850.9651323 1
 
< 0.1%
846.1493961 1
 
< 0.1%
843.2506836 124
 
0.2%

13171_FERM0101.Single_Use_pH_PV
Real number (ℝ)

MISSING 

Distinct1403
Distinct (%)2.9%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean628.81506
Minimum-790.12803
Maximum805.952
Zeros0
Zeros (%)0.0%
Negative480
Negative (%)0.9%
Memory size2.8 MiB
2024-09-29T20:18:23.084103image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-790.12803
5-th percentile5.7920002
Q1799.91201
median800.08799
Q3800.28799
95-th percentile800.48799
Maximum805.952
Range1596.08
Interquartile range (IQR)0.37597656

Descriptive statistics

Standard deviation345.26044
Coefficient of variation (CV)0.54906515
Kurtosis1.9572604
Mean628.81506
Median Absolute Deviation (MAD)0.2
Skewness-1.7413328
Sum30029063
Variance119204.77
MonotonicityNot monotonic
2024-09-29T20:18:23.158772image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
800.4 3979
 
7.6%
799.9919922 3088
 
5.9%
800.0879883 2570
 
4.9%
800.4879883 1776
 
3.4%
800.1839844 1623
 
3.1%
800.047998 1504
 
2.9%
800.1759766 1331
 
2.5%
799.9679688 1321
 
2.5%
800.3199707 1239
 
2.4%
800.1919922 1221
 
2.3%
Other values (1393) 28103
53.6%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
-790.1280273 352
0.7%
-788.4639648 1
 
< 0.1%
-788.308278 1
 
< 0.1%
-788.3039551 5
 
< 0.1%
-788.2997672 1
 
< 0.1%
-788.2997318 1
 
< 0.1%
-788.2997013 1
 
< 0.1%
-788.2996668 1
 
< 0.1%
-788.2996653 1
 
< 0.1%
-788.2996517 1
 
< 0.1%
ValueCountFrequency (%)
805.952002 442
 
0.8%
802.352002 664
 
1.3%
800.5759766 561
 
1.1%
800.5199707 560
 
1.1%
800.4879883 1776
3.4%
800.4080078 472
 
0.9%
800.4 3979
7.6%
800.3759766 586
 
1.1%
800.352002 545
 
1.0%
800.3199707 1239
 
2.4%

13171_FERM0101.Temperatura_PV
Real number (ℝ)

MISSING 

Distinct30799
Distinct (%)64.5%
Missing4658
Missing (%)8.9%
Infinite0
Infinite (%)0.0%
Mean17.07861
Minimum0.15725258
Maximum80.640002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 MiB
2024-09-29T20:18:23.237779image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.15725258
5-th percentile3.4799988
Q113.499294
median16.519995
Q320.359998
95-th percentile29.653087
Maximum80.640002
Range80.48275
Interquartile range (IQR)6.8607034

Descriptive statistics

Standard deviation7.7622566
Coefficient of variation (CV)0.45450167
Kurtosis-0.39970991
Mean17.07861
Median Absolute Deviation (MAD)3.2799927
Skewness0.05152108
Sum815589
Variance60.252628
MonotonicityNot monotonic
2024-09-29T20:18:23.313055image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.27199707 157
 
0.3%
29.26400146 140
 
0.3%
29.74399414 140
 
0.3%
29.76800537 137
 
0.3%
29.29599609 127
 
0.2%
3.223999023 120
 
0.2%
29.24000244 118
 
0.2%
3.544000244 113
 
0.2%
3.423999023 108
 
0.2%
3.584002686 106
 
0.2%
Other values (30789) 46489
88.7%
(Missing) 4658
 
8.9%
ValueCountFrequency (%)
0.1572525757 1
 
< 0.1%
3.015997314 1
 
< 0.1%
3.05448312 1
 
< 0.1%
3.130215251 1
 
< 0.1%
3.144000244 1
 
< 0.1%
3.158182998 1
 
< 0.1%
3.167999268 1
 
< 0.1%
3.176000977 10
< 0.1%
3.176016301 1
 
< 0.1%
3.17632069 1
 
< 0.1%
ValueCountFrequency (%)
80.64000244 1
 
< 0.1%
74.84973373 1
 
< 0.1%
31.48800049 1
 
< 0.1%
31.44858354 1
 
< 0.1%
31.44000244 2
 
< 0.1%
31.40799561 1
 
< 0.1%
31.39545621 1
 
< 0.1%
31.38399658 5
< 0.1%
31.38026083 1
 
< 0.1%
31.37964891 1
 
< 0.1%

Interactions

2024-09-29T20:18:19.465747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:01.367026image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:03.261584image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:04.376733image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:05.480809image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:06.543488image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:07.601257image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:08.714589image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:09.844578image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:10.907260image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:11.984933image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:13.069764image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-09-29T20:18:07.400814image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:08.500712image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:09.630268image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:10.705487image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:11.779742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:12.863982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:14.898378image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:15.967031image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:17.037902image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:18.102549image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:19.235293image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:20.434784image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:03.111962image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:04.229530image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:05.327049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:06.404592image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:07.462581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:08.566756image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:09.697094image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:10.767763image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:11.842414image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:12.927095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:14.960745image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:16.027878image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:17.102047image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:18.164288image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:19.302263image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:20.510399image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:03.186561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:04.305467image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:05.408911image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:06.475098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:07.531918image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:08.641120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:09.772379image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:10.837261image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:11.913249image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:12.999885image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:15.031519image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:16.098945image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:17.172957image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:18.240536image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-09-29T20:18:19.375593image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-09-29T20:18:23.372425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
13171_FERM0101.Agitation_PV13171_FERM0101.Air_Sparge_PV13171_FERM0101.Biocontainer_Pressure_PV13171_FERM0101.DO_1_PV13171_FERM0101.DO_2_PV13171_FERM0101.Gas_Overlay_PV13171_FERM0101.Load_Cell_Net_PV13171_FERM0101.PUMP_1_PV13171_FERM0101.PUMP_1_TOTAL13171_FERM0101.PUMP_2_PV13171_FERM0101.PUMP_2_TOTAL13171_FERM0101.Single_Use_DO_PV13171_FERM0101.Single_Use_pH_PV13171_FERM0101.Temperatura_PV13171_FERM0101.pH_1_PV13171_FERM0101.pH_2_PV
13171_FERM0101.Agitation_PV1.0000.262-0.5520.502-0.0190.6470.8040.000-0.1860.181-0.386-0.138-0.2400.1640.6250.021
13171_FERM0101.Air_Sparge_PV0.2621.000-0.0130.447-0.0040.1720.216-0.0020.0030.250-0.012-0.259-0.2280.2440.1750.019
13171_FERM0101.Biocontainer_Pressure_PV-0.552-0.0131.000-0.0640.017-0.665-0.711-0.0110.410-0.0220.474-0.2540.0920.237-0.3590.027
13171_FERM0101.DO_1_PV0.5020.447-0.0641.000NaN0.3070.405-0.0040.0160.243-0.053-0.455-0.4020.4560.3590.049
13171_FERM0101.DO_2_PV-0.019-0.0040.017NaN1.000-0.021-0.008-0.0000.023-0.003-0.022-0.0230.019-0.019NaNNaN
13171_FERM0101.Gas_Overlay_PV0.6470.172-0.6650.307-0.0211.0000.6950.007-0.3440.095-0.4530.077-0.1910.0280.4350.004
13171_FERM0101.Load_Cell_Net_PV0.8040.216-0.7110.405-0.0080.6951.0000.003-0.2170.121-0.4490.034-0.2740.0530.5480.020
13171_FERM0101.PUMP_1_PV0.000-0.002-0.011-0.004-0.0000.0070.0031.0000.0010.016-0.0030.0080.0080.0030.0020.003
13171_FERM0101.PUMP_1_TOTAL-0.1860.0030.4100.0160.023-0.344-0.2170.0011.0000.0130.422-0.1710.2300.045-0.024-0.029
13171_FERM0101.PUMP_2_PV0.1810.250-0.0220.243-0.0030.0950.1210.0160.0131.0000.101-0.173-0.1660.1710.0870.013
13171_FERM0101.PUMP_2_TOTAL-0.386-0.0120.474-0.053-0.022-0.453-0.449-0.0030.4220.1011.000-0.097-0.0790.036-0.2590.125
13171_FERM0101.Single_Use_DO_PV-0.138-0.259-0.254-0.455-0.0230.0770.0340.008-0.171-0.173-0.0971.0000.276-0.420-0.0470.014
13171_FERM0101.Single_Use_pH_PV-0.240-0.2280.092-0.4020.019-0.191-0.2740.0080.230-0.166-0.0790.2761.000-0.217-0.114-0.160
13171_FERM0101.Temperatura_PV0.1640.2440.2370.456-0.0190.0280.0530.0030.0450.1710.036-0.420-0.2171.0000.1120.133
13171_FERM0101.pH_1_PV0.6250.175-0.3590.359NaN0.4350.5480.002-0.0240.087-0.259-0.047-0.1140.1121.0000.038
13171_FERM0101.pH_2_PV0.0210.0190.0270.049NaN0.0040.0200.003-0.0290.0130.1250.014-0.1600.1330.0381.000

Missing values

2024-09-29T20:18:20.591376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-29T20:18:20.742853image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-09-29T20:18:20.938684image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

13171_FERM0101.Agitation_PV13171_FERM0101.Air_Sparge_PV13171_FERM0101.Biocontainer_Pressure_PV13171_FERM0101.DO_1_PV13171_FERM0101.DO_2_PV13171_FERM0101.Gas_Overlay_PV13171_FERM0101.Load_Cell_Net_PV13171_FERM0101.pH_1_PV13171_FERM0101.pH_2_PV13171_FERM0101.PUMP_1_PV13171_FERM0101.PUMP_1_TOTAL13171_FERM0101.PUMP_2_PV13171_FERM0101.PUMP_2_TOTAL13171_FERM0101.Single_Use_DO_PV13171_FERM0101.Single_Use_pH_PV13171_FERM0101.Temperatura_PV
DateTime
2023-03-15 00:00:00.0000.00.0480.00.00.00.0-6.6400001.8691043.20.017.7733260.011066.217969799.991992800.29599619.130649
2023-03-15 00:15:00.0000.00.0480.00.00.00.0-6.6400001.8691043.20.017.7733260.011066.217969799.991992800.29599619.117385
2023-03-15 00:30:00.0000.00.0480.00.00.00.0-6.6383551.8691043.20.017.7733260.011066.217969799.991992800.29599619.128038
2023-03-15 00:45:00.0000.00.0480.00.00.00.0-6.6400001.8691043.20.017.7733260.011066.217969799.991992800.29599619.127873
2023-03-15 01:00:00.0000.00.0480.00.00.00.0-6.6400001.8691043.20.017.7733260.011066.217969799.991992800.29599619.080857
2023-03-15 01:15:00.0000.00.0480.00.00.00.0-6.6400001.8691043.20.017.7733260.011066.217969799.991992800.29599619.113166
2023-03-15 01:30:00.0000.00.0480.00.00.00.0-2.1818861.8691043.20.017.7733260.011066.217969799.991992800.29599619.081337
2023-03-15 01:45:00.0000.00.0480.00.00.00.0-6.5875421.8691043.20.017.7733260.011066.217969799.991992800.29599619.128574
2023-03-15 02:00:00.0000.00.0480.00.00.00.0-6.6400001.8691043.20.017.7733260.011066.217969799.991992800.29599619.072420
2023-03-15 02:15:00.0000.00.0480.00.00.00.0-6.6400001.8691043.20.017.7733260.011066.217969799.991992800.29599618.965598
13171_FERM0101.Agitation_PV13171_FERM0101.Air_Sparge_PV13171_FERM0101.Biocontainer_Pressure_PV13171_FERM0101.DO_1_PV13171_FERM0101.DO_2_PV13171_FERM0101.Gas_Overlay_PV13171_FERM0101.Load_Cell_Net_PV13171_FERM0101.pH_1_PV13171_FERM0101.pH_2_PV13171_FERM0101.PUMP_1_PV13171_FERM0101.PUMP_1_TOTAL13171_FERM0101.PUMP_2_PV13171_FERM0101.PUMP_2_TOTAL13171_FERM0101.Single_Use_DO_PV13171_FERM0101.Single_Use_pH_PV13171_FERM0101.Temperatura_PV
DateTime
2024-09-10 21:45:00.00020.00.0-2.0399290.00.04.000084161.65.049667-0.0163510.07.4399990.00.0799.991992800.43.527186
2024-09-10 22:00:00.00020.00.0-2.0804380.00.03.999963161.65.057235-0.0163510.07.4399990.00.0799.991992800.43.532234
2024-09-10 22:15:00.00020.00.0-2.0196780.00.04.000033161.65.064804-0.0163510.07.4399990.00.0799.991992800.43.479999
2024-09-10 22:30:00.00020.00.0-1.9589110.00.03.999917161.65.064804-0.0163510.07.4399990.00.0799.991992800.43.503998
2024-09-10 22:45:00.00020.00.0-2.0399290.00.04.000055161.65.065084-0.0163510.07.4399990.00.0799.991992800.43.496921
2024-09-10 23:00:00.00020.00.0-2.0289130.00.03.999960161.65.065084-0.0163510.07.4399990.00.0799.991992800.43.549670
2024-09-10 23:15:00.00020.00.0-2.0804380.00.03.999818161.65.064999-0.0163510.07.4399990.00.0799.991992800.43.474442
2024-09-10 23:30:00.00020.00.0-2.0523840.00.03.999684161.65.065084-0.0163510.07.4399990.00.0799.991992800.43.487383
2024-09-10 23:45:00.00020.00.0-2.0804380.00.03.999840161.65.065084-0.0163510.07.4399990.00.0799.991992800.43.479999
2024-09-11 00:00:00.00020.00.0-2.0520070.00.03.999893161.65.072766-0.0163510.07.4399990.00.0799.991992800.43.471997

Duplicate rows

Most frequently occurring

13171_FERM0101.Agitation_PV13171_FERM0101.Air_Sparge_PV13171_FERM0101.Biocontainer_Pressure_PV13171_FERM0101.DO_1_PV13171_FERM0101.DO_2_PV13171_FERM0101.Gas_Overlay_PV13171_FERM0101.Load_Cell_Net_PV13171_FERM0101.pH_1_PV13171_FERM0101.pH_2_PV13171_FERM0101.PUMP_1_PV13171_FERM0101.PUMP_1_TOTAL13171_FERM0101.PUMP_2_PV13171_FERM0101.PUMP_2_TOTAL13171_FERM0101.Single_Use_DO_PV13171_FERM0101.Single_Use_pH_PV13171_FERM0101.Temperatura_PV# duplicates
894NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4655
3080.00.0480.00.00.00.0-6.56NaN3.2000000.017.3600010.00.0712.941797800.426.04000228
3090.00.0480.00.00.00.0-6.56NaN3.2000000.017.3600010.00.0712.941797800.426.05600621
4910.00.0480.00.00.00.0-6.40NaN3.2000000.017.3600010.00.0712.941797800.424.72800313
1400.00.0480.00.00.00.0-6.801.457041-0.0163510.017.3600010.00.0712.941797800.414.45600612
6450.00.0480.00.00.00.0-6.40NaN3.2000000.017.3600010.00.0712.941797800.429.06400112
6600.00.0480.00.00.00.0-6.32NaN3.2000000.017.3600010.00.0712.941797800.428.00799612
1480.00.0480.00.00.00.0-6.801.457041-0.0163510.017.3600010.00.0712.941797800.414.55999811
2390.00.0480.00.00.00.0-6.64NaN3.2000000.017.3600010.00.0712.941797800.414.91999511
3070.00.0480.00.00.00.0-6.56NaN3.2000000.017.3600010.00.0712.941797800.426.02399911